A Hybrid Approach for Thread Recommendation in MOOC Forums

Authors: Ahmad. A. Kardan, Amir Narimani, Foozhan Ataiefard

Abstract:

Recommender Systems have been developed to provide contents and services compatible to users based on their behaviors and interests. Due to information overload in online discussion forums and users diverse interests, recommending relative topics and threads is considered to be helpful for improving the ease of forum usage. In order to lead learners to find relevant information in educational forums, recommendations are even more needed. We present a hybrid thread recommender system for MOOC forums by applying social network analysis and association rule mining techniques. Initial results indicate that the proposed recommender system performs comparatively well with regard to limited available data from users' previous posts in the forum.

Keywords: Association rule mining, hybrid recommender system, massive open online courses, MOOCs, social network analysis.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132317

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